Microbiome assessment for A. cervicornis microplastics project (16S and ITS2)
Last run on 01 August 2022
16S Assessment
I am largely following the DADA2 tutorial by Benjamin Callahan that can be found here.
Inspecting the data
Check samples to use
## [1] "A10" "A11" "A12" "A2" "A3" "A6" "A8" "A9" "B10" "B11" "B3" "B4"
## [13] "B5" "B7" "C1" "C10" "C11" "C2" "C4" "G5" "G6"
View example quality plots
Viewing the quality of 2 of the forward samples 
Viewing the quality of the same 2 of the reverse samples 
Filter and trim samples
Perform the filtering
You can see in above figures that the quality of reads drop off towards the end, so we need to filter out these low quality reads
## reads.in reads.out
## A10_16S_R1.fastq 25343 23991
## A11_16S_R1.fastq 27398 26006
## A12_16S_R1.fastq 45822 44152
## A2_16S_R1.fastq 99390 94836
## A3_16S_R1.fastq 77568 73174
## A6_16S_R1.fastq 44719 41897After filtering out the low quality reads, we have maintained about 95.7 of the original reads.
Check the filtering
Viewing the quality of the same 2 of the forward samples post filtering and trimming 
Viewing the quality of the same 2 of the reverse samples post filtering and trimming 
DADA2 sample processing
Error Rates
The error rates for each possible transition (A→C, A→G, …) are shown. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence of the machine-learning algorithm. The red line shows the error rates expected under the nominal definition of the Q-score. We want the estimated error rates (black line) to be a good fit to the observed rates (points), and the error rates to drop with increased quality.
Sample Inference
We are now ready to apply the core sample inference algorithm to the filtered and trimmed sequence data.
Forward sample inference
## Sample 1 - 23991 reads in 6094 unique sequences.
## Sample 2 - 26006 reads in 6312 unique sequences.
## Sample 3 - 44152 reads in 7218 unique sequences.
## Sample 4 - 94836 reads in 13509 unique sequences.
## Sample 5 - 73174 reads in 9644 unique sequences.
## Sample 6 - 41897 reads in 7509 unique sequences.
## Sample 7 - 48504 reads in 7342 unique sequences.
## Sample 8 - 32048 reads in 7630 unique sequences.
## Sample 9 - 24910 reads in 5725 unique sequences.
## Sample 10 - 34782 reads in 5737 unique sequences.
## Sample 11 - 34374 reads in 5211 unique sequences.
## Sample 12 - 90460 reads in 8319 unique sequences.
## Sample 13 - 23566 reads in 5815 unique sequences.
## Sample 14 - 110660 reads in 10438 unique sequences.
## Sample 15 - 27927 reads in 5388 unique sequences.
## Sample 16 - 73356 reads in 7647 unique sequences.
## Sample 17 - 23594 reads in 6498 unique sequences.
## Sample 18 - 25590 reads in 6768 unique sequences.
## Sample 19 - 42054 reads in 7901 unique sequences.
## Sample 20 - 1795 reads in 421 unique sequences.
## Sample 21 - 162 reads in 63 unique sequences.Inspecting the returned dada-class object for the first forward sample:
## dada-class: object describing DADA2 denoising results
## 113 sequence variants were inferred from 6094 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16The DADA2 algorithm inferred 144 true sequence variants from the 9515 unique sequences in the first sample. There is much more to the dada-class return object than this (see help("dada-class") for some info), including multiple diagnostics about the quality of each denoised sequence variant, but that is beyond the scope of an introductory tutorial.
Reverse sample inference
## Sample 1 - 23991 reads in 5924 unique sequences.
## Sample 2 - 26006 reads in 6092 unique sequences.
## Sample 3 - 44152 reads in 7108 unique sequences.
## Sample 4 - 94836 reads in 12281 unique sequences.
## Sample 5 - 73174 reads in 8686 unique sequences.
## Sample 6 - 41897 reads in 7149 unique sequences.
## Sample 7 - 48504 reads in 7069 unique sequences.
## Sample 8 - 32048 reads in 7283 unique sequences.
## Sample 9 - 24910 reads in 5526 unique sequences.
## Sample 10 - 34782 reads in 5706 unique sequences.
## Sample 11 - 34374 reads in 4744 unique sequences.
## Sample 12 - 90460 reads in 8211 unique sequences.
## Sample 13 - 23566 reads in 5706 unique sequences.
## Sample 14 - 110660 reads in 10194 unique sequences.
## Sample 15 - 27927 reads in 5233 unique sequences.
## Sample 16 - 73356 reads in 7528 unique sequences.
## Sample 17 - 23594 reads in 6469 unique sequences.
## Sample 18 - 25590 reads in 6315 unique sequences.
## Sample 19 - 42054 reads in 7772 unique sequences.
## Sample 20 - 1795 reads in 465 unique sequences.
## Sample 21 - 162 reads in 69 unique sequences.
Merge paired reads
We now merge the forward and reverse reads together to obtain the full denoised sequences. Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding denoised reverse reads, and then constructing the merged “contig” sequences. By default, merged sequences are only output if the forward and reverse reads overlap by at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).
The mergers object is a list of data.frames from each sample. Each data.frame contains the merged sequence, its abundance, and the indices of the forward and reverse sequence variants that were merged. Paired reads that did not exactly overlap were removed by mergePairs, further reducing spurious output.
Construct sequence table
We can now construct an amplicon sequence variant table (ASV) table, a higher-resolution version of the OTU table produced by traditional methods.
##
## 223 227 239 246 251 252 253 254 255 256 257
## 2 1 1 1 5 46 1601 86 4 4 2
The sequence table is a matrix with rows corresponding to (and named by) the samples, and columns corresponding to (and named by) the sequence variants. This table contains 1753 ASVs.
After viewing the distribution of read lengths, it looks like we have some that fall outside the expected range (244 - 264) so we will go ahead and remove these non target length sequences.
##
## 246 251 252 253 254 255 256 257
## 1 5 46 1601 86 4 4 2This updated table now contains 1749 ASVs.
Remove chimeras
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs. Chimeric sequences are identified if they can be exactly reconstructed by combining a left-segment and a right-segment from two more abundant “parent” sequences.
A total of 9 bimeras were identified from the 1740 input sequences, thus retaining 100% of sequences.
Track reads through the pipeline
As a final check of our progress, we can look at the number of reads that made it through each step in the pipeline:
| Genotype | Treatment | Input | Filtered | Denoised Forward | Denoised Reverse | Merged | Nonchimera |
|---|---|---|---|---|---|---|---|
| 6 | OAW MP+ | 25343 | 23991 | 23882 | 23969 | 23811 | 23811 |
| 5 | OAW Control | 27398 | 26006 | 25982 | 25971 | 25961 | 25961 |
| 5a | AMB MP+ | 45822 | 44152 | 44079 | 44127 | 43982 | 43982 |
| 10a | OAW MP+ | 99390 | 94836 | 94654 | 94676 | 94353 | 94205 |
| 5a | AMB Control | 77568 | 73174 | 73129 | 73130 | 73018 | 73018 |
| 10a | AMB MP+ | 44719 | 41897 | 41808 | 41848 | 41496 | 41496 |
| 6 | AMB Control | 50868 | 48504 | 48363 | 48414 | 48114 | 48073 |
| 5a | OAW Control | 33629 | 32048 | 31998 | 32015 | 31874 | 31842 |
| 5a | OAW MP+ | 26111 | 24910 | 24868 | 24838 | 24666 | 24641 |
| 5 | AMB Control | 36048 | 34782 | 34765 | 34754 | 34710 | 34710 |
| 2 | AMB MP+ | 36469 | 34374 | 34349 | 34353 | 34289 | 34289 |
| 2 | AMB Control | 93851 | 90460 | 90421 | 90323 | 90201 | 90201 |
| 6 | AMB MP+ | 24767 | 23566 | 23506 | 23540 | 23364 | 23328 |
| 10a | OAW Control | 114866 | 110660 | 110570 | 110599 | 110465 | 110452 |
| 5 | AMB MP+ | 29051 | 27927 | 27900 | 27896 | 27851 | 27851 |
| 2 | OAW MP+ | 75311 | 73356 | 73246 | 73255 | 72852 | 72852 |
| 6 | OAW Control | 24571 | 23594 | 23583 | 23578 | 23571 | 23571 |
| 10a | AMB Control | 26776 | 25590 | 25551 | 25561 | 25491 | 25491 |
| 5 | OAW MP+ | 43598 | 42054 | 42021 | 42021 | 41807 | 41723 |
| NA | blank_control | 1918 | 1795 | 1792 | 1792 | 1792 | 1792 |
| NA | blank_control | 169 | 162 | 153 | 158 | 153 | 153 |
Assign taxonomy
It is common at this point, especially in 16S/18S/ITS amplicon sequencing, to assign taxonomy to the sequence variants. The DADA2 package provides a native implementation of the naive Bayesian classifier method for this purpose. The assignTaxonomy function takes as input a set of sequences to be classified and a training set of reference sequences with known taxonomy, and outputs taxonomic assignments with at least minBoot bootstrap confidence.
The dada2 package GitHub maintains the most updated versions of the Silva databases., but I downloaded the databases from the associated Zenodo. The versions in this GitHub repository, used here, were last updated on 26 July 2022.
Let’s inspect the taxonomic assignments:
## Kingdom Phylum Class Order
## [1,] "Bacteria" "Proteobacteria" "Alphaproteobacteria" "Rickettsiales"
## [2,] "Bacteria" "Cyanobacteria" "Cyanobacteriia" "Chloroplast"
## [3,] "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Pseudomonadales"
## [4,] "Bacteria" "Myxococcota" "Myxococcia" "Myxococcales"
## [5,] "Bacteria" "Campylobacterota" "Campylobacteria" "Campylobacterales"
## [6,] "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Burkholderiales"
## Family Genus Species
## [1,] "Fokiniaceae" "MD3-55" NA
## [2,] NA NA NA
## [3,] "Pseudomonadaceae" "Pseudomonas" NA
## [4,] "Myxococcaceae" "P3OB-42" NA
## [5,] NA NA NA
## [6,] "Alcaligenaceae" "Alcaligenes" NAGreat! We can now save this and hand it off to Phyloseq for further analyses.
Remove contaminations
Remove mitochondria, chloroplasts, and non-bacteria
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1610 taxa and 21 samples ]
## sample_data() Sample Data: [ 21 samples by 5 sample variables ]
## tax_table() Taxonomy Table: [ 1610 taxa by 7 taxonomic ranks ]Removal of mitochondira, chloroplasts, and non-bacteria taxa reduced the total number of taxa from 1740 to 1610.
Remove neg control contamination
Just 3 out of the 1610 ASVs were classified as contaminants.
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1607 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 1607 taxa by 7 taxonomic ranks ]
Blast NCBI
I have not rerun this portion since only running the pipeline with the 19 GE samples!
Looks good! We have now cleaned up the sample data to remove contamination from non target organisms and those from the negative controls.
Rarefy
Rarefy cleaned data
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1538 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 1538 taxa by 8 taxonomic ranks ]## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1514 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 1514 taxa by 8 taxonomic ranks ]
Trim underrepresented OUTs
## [1] "samples with counts below z-score -2.5 :"
## character(0)
## [1] "zscores:"
## named numeric(0)
## [1] "OTUs passing frequency cutoff 1e-04 : 416"
## [1] "OTUs with counts in 0.02 of samples:"
##
## TRUE
## 416## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 416 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 416 taxa by 8 taxonomic ranks ]
Rarefy trimmed data
## named numeric(0)
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 416 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 416 taxa by 8 taxonomic ranks ]
Microbiome diversity analyses
Alpha metrics
Notes from phyloseq author Visualize alpha-diversity - Should be done on raw, untrimmed dataset
Simpson diversity
Shannon diversity
Core vs. accessory microbiome
Core
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 10 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 6 sample variables ]
## tax_table() Taxonomy Table: [ 10 taxa by 8 taxonomic ranks ]
Core stats
Accessory
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 406 taxa and 19 samples ]
## sample_data() Sample Data: [ 19 samples by 5 sample variables ]
## tax_table() Taxonomy Table: [ 406 taxa by 8 taxonomic ranks ]
Accessory stats
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 0.0002995 0.00009984 0.117 0.9487
## Residuals 15 0.0127979 0.00085319##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 0.0002995 0.00009984 0.117 999 0.933
## Residuals 15 0.0127979 0.00085319
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## AMB Control AMB MP+ OAW Control OAW MP+
## AMB Control 0.60700 0.52400 0.791
## AMB MP+ 0.59848 0.94400 0.762
## OAW Control 0.54900 0.93876 0.827
## OAW MP+ 0.80260 0.79523 0.83516
##
## Call:
## adonis(formula = seq.acc ~ Treatment, data = samdf.acc, permutations = 999)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Treatment 3 1.0761 0.35869 0.79026 0.13648 0.993
## Residuals 15 6.8083 0.45388 0.86352
## Total 18 7.8843 1.00000
Data visualization
Preliminary figures
Taking phyloseq data and making some preliminary visualizations based on DADA2 tutorial:
Principal component analyses
All microbiome
Core microbiome
Accessory microbiome
Shannon diversity
Simpson diversity
ITS2 Assessment
Abundance by treatment and genotype
Figure SXX. Relative abundance of major ITS2 types by coral fragment (x axis) and treatment (facets). Light green represent Symbiodinium spp. (A3) and dark green represents Breviolum spp. (B2).
Abundance by treatment only
Figure SXX. Relative abundance of major ITS2 types grouped by treatment (n = 4-5 corals per treatment). Light green represent Symbiodinium spp. (A3) and dark green represents Breviolum spp. (B2).
Manuscript Materials
Methods
Bacterial and Symbiodiniaceae metabarcoding
We characterized the coral microbial and Symbiodiniaceae community composition using 16S and ITS2 metabarcoding, respectively, to target the V4/V5 region following (43). Pooled libraries were sequenced on an Illumina Miseq (paired-end 250 bp) at Tufts Sequencing facility. P
16S and ITS2 data were pre-processed in bbmap (36) and cutadapt (37) to remove primer sequences. DADA2 (38) truncated reads, calculated error rates, de-duplicated reads, inferred sequence variants, merged paired reads, and removed bimeras (38) (see table SXX for read counts through processing).
16S taxonomy was assigned using the Silva v132 dataset (44) and ITS2 taxonomy was assigned through submission to Symportal (CITE). Using phyloseq (41), ASVs assigning to family “Mitochondria”, order “Chloroplast”, or those failing to assign to kingdom “Bacteria” were removed. Using vegan (29), the ASV table was rarefied to 12,000 reads, which retained ~90% of samples (9 samples removed; Table S2). Phyloseq (41) calculated three diversity metrics: ASV richness, Shannon index, and inverse of Simpson’s index. ASV richness and inverse Simpson index were log-transformed and a one-way ANOVA with Tukey HSD tests compared diversity metrics across sites and reef zones. MCMC.OTU (42) then trimmed ASVs representing <0.01% of counts or only present in one sample. Linear regressions assessed correlations between alpha diversity metrics and colony size. Sample dissimilarity was evaluated for the total bacterial community as described for ITS2. These analyses were then repeated on 16S data separated by microbiome (45) into core (present in >70% of samples) and accessory components, and finally repeated using unrarefied, relative ASV abundances to confirm results.
metabarcoding
Methods text goes here
Results
Results text goes here
Figures
Supplemental figures
Supplemental tables
Session Information
All code was written by Colleen B. Bove, feel free to contact with questions.
Session information from the last run date on 01 August 2022:
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] awtools_0.2.1 microbiome_1.8.0
## [3] ggpubr_0.4.0 MCMC.OTU_1.0.10
## [5] MCMCglmm_2.33 ape_5.6-1
## [7] coda_0.19-4 Matrix_1.3-4
## [9] janitor_2.1.0 vegan_2.5-7
## [11] lattice_0.20-45 permute_0.9-7
## [13] plotly_4.10.0 RColorBrewer_1.1-3
## [15] decontam_1.6.0 phyloseq_1.30.0
## [17] kableExtra_1.3.4 ShortRead_1.44.3
## [19] GenomicAlignments_1.22.1 SummarizedExperiment_1.16.1
## [21] DelayedArray_0.12.3 matrixStats_0.62.0
## [23] Biobase_2.46.0 Rsamtools_2.2.3
## [25] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
## [27] Biostrings_2.54.0 XVector_0.26.0
## [29] IRanges_2.20.2 S4Vectors_0.24.4
## [31] BiocParallel_1.20.1 BiocGenerics_0.32.0
## [33] forcats_0.5.1 stringr_1.4.0
## [35] dplyr_1.0.8 purrr_0.3.4
## [37] readr_2.1.2 tidyr_1.2.0
## [39] tibble_3.1.7 ggplot2_3.3.6
## [41] tidyverse_1.3.1 dada2_1.20.0
## [43] Rcpp_1.0.9 knitr_1.33
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.4.1 systemfonts_1.0.2
## [4] plyr_1.8.7 igraph_1.2.11 lazyeval_0.2.2
## [7] splines_3.6.3 crosstalk_1.2.0 digest_0.6.29
## [10] foreach_1.5.2 htmltools_0.5.2 fansi_1.0.3
## [13] magrittr_2.0.3 cluster_2.1.2 tzdb_0.2.0
## [16] modelr_0.1.8 RcppParallel_5.1.5 svglite_2.1.0
## [19] jpeg_0.1-9 colorspace_2.0-3 rvest_1.0.1
## [22] textshaping_0.3.6 haven_2.4.3 xfun_0.29
## [25] crayon_1.5.1 RCurl_1.98-1.7 jsonlite_1.7.2
## [28] survival_3.2-13 iterators_1.0.14 glue_1.6.2
## [31] gtable_0.3.0 zlibbioc_1.32.0 webshot_0.5.2
## [34] car_3.1-0 Rhdf5lib_1.8.0 abind_1.4-5
## [37] scales_1.2.0 DBI_1.1.3 rstatix_0.7.0
## [40] viridisLite_0.4.0 htmlwidgets_1.5.4 httr_1.4.3
## [43] ellipsis_0.3.2 farver_2.1.1 pkgconfig_2.0.3
## [46] sass_0.4.0 dbplyr_2.1.1 deldir_1.0-6
## [49] utf8_1.2.2 labeling_0.4.2 tidyselect_1.1.1
## [52] rlang_1.0.4 reshape2_1.4.4 munsell_0.5.0
## [55] cellranger_1.1.0 tools_3.6.3 cachem_1.0.6
## [58] cli_3.3.0 generics_0.1.3 ade4_1.7-18
## [61] broom_1.0.0 evaluate_0.15 biomformat_1.14.0
## [64] fastmap_1.1.0 ragg_1.1.3 yaml_2.3.5
## [67] fs_1.5.2 nlme_3.1-155 xml2_1.3.3
## [70] compiler_3.6.3 rstudioapi_0.13 png_0.1-7
## [73] ggsignif_0.6.3 reprex_2.0.1 bslib_0.4.0
## [76] stringi_1.7.8 highr_0.9 cubature_2.0.4.2
## [79] tensorA_0.36.2 multtest_2.42.0 vctrs_0.4.1
## [82] pillar_1.8.0 lifecycle_1.0.1 jquerylib_0.1.4
## [85] cowplot_1.1.1 data.table_1.14.2 bitops_1.0-7
## [88] corpcor_1.6.10 R6_2.5.1 latticeExtra_0.6-30
## [91] hwriter_1.3.2.1 gridExtra_2.3 codetools_0.2-18
## [94] MASS_7.3-55 assertthat_0.2.1 rhdf5_2.30.1
## [97] withr_2.5.0 GenomeInfoDbData_1.2.2 mgcv_1.8-38
## [100] hms_1.1.1 grid_3.6.3 rmarkdown_2.13
## [103] snakecase_0.11.0 carData_3.0-5 Rtsne_0.15
## [106] lubridate_1.8.0 interp_1.0-33